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Comparison · Infra & APIs
FoRecoML vs SLmetrics
A side-by-side editorial comparison of FoRecoML and SLmetrics — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:machine-learningr-package
FoRecoML vs SLmetrics: at a glance
What is FoRecoML?
The machine-learning arm of a forecast reconciliation toolkit, four months old and already sharing its sibling's plumbing.
FoRecoML brings machine-learning approaches to forecast reconciliation across cross-sectional, temporal, and cross-temporal frameworks through csrml(), terml(), and ctrml(). It reached CRAN in April 2026 and has since spent both releases integrating with FoReco rather than expanding its own method set: results are now FoReco's foreco objects, and print() and summary() report framework, approach, problem dimensions, features, training sample size, combination matrix, and trained models.
What is SLmetrics?
A young ML metrics package rewrote its own backend twice in six months chasing speed.
SLmetrics provides supervised learning evaluation metrics for R — confusion matrices, classification and regression measures, ROC and precision-recall curves — with the computation pushed into C++. After a series of pre-releases it now runs on an Armadillo backend, supports OpenMP parallelism and LAPACK/BLAS, and reports 5-20x speedups over its earlier implementations. The API has been reshaped for extensibility, with an estimator argument replacing the fixed aggregation options.
FoRecoML vs SLmetrics: editorial side-by-side
FoRecoML
0.0INFRA · APIS
The machine-learning arm of a forecast reconciliation toolkit, four months old and already sharing its sibling's plumbing.
◆ Current state
FoRecoML brings machine-learning approaches to forecast reconciliation across cross-sectional, temporal, and cross-temporal frameworks through csrml(), terml(), and ctrml(). It reached CRAN in April 2026 and has since spent both releases integrating with FoReco rather than expanding its own method set: results are now FoReco's foreco objects, and print() and summary() report framework, approach, problem dimensions, features, training sample size, combination matrix, and trained models.
◆ Where it's heading
This package is being built as a satellite, not a competitor. Adopting FoReco's exported new_foreco_class() constructor within days of that class appearing means FoRecoML results drop straight into the same print, summary, plot, and components methods as analytically reconciled ones — which is what makes machine-learning and classical reconciliation directly comparable in a single workflow. The 1.1.1 argument-validation work landed in the same minute as the equivalent change in FoReco, so the two are being maintained as one release train.
◆ Prediction
With the integration work done, the next release is more likely to add or expose machine-learning approaches than to keep reshaping output; the structured summary already enumerates features and trained models, which suggests inspection tooling is where attention has been.
SLmetrics
0.0INFRA · APIS
A young ML metrics package rewrote its own backend twice in six months chasing speed.
◆ Current state
SLmetrics provides supervised learning evaluation metrics for R — confusion matrices, classification and regression measures, ROC and precision-recall curves — with the computation pushed into C++. After a series of pre-releases it now runs on an Armadillo backend, supports OpenMP parallelism and LAPACK/BLAS, and reports 5-20x speedups over its earlier implementations. The API has been reshaped for extensibility, with an estimator argument replacing the fixed aggregation options.
◆ Where it's heading
Every release in this timeline is about making the same metrics compute faster or compose better. The backend moved from Rcpp to plain C++, gained OpenMP, then was ported wholesale from Eigen to Armadillo with heavy templating. In parallel the author has been widening the API's joints: generic S3 signatures, an extensible estimator argument, and function signatures loose enough that wrapping packages can rename arguments. Bundled datasets and embedded formulas in the docs point at teaching and benchmarking use. The package still labels itself pre-release, which is consistent with how freely it has broken argument names along the way.
◆ Prediction
A stable non-pre-release version is the natural next step now that the backend has settled on Armadillo, though the repeated willingness to rename arguments suggests more API churn may come first.
Alternatives to FoRecoML and SLmetrics
Other Infra & APIs products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either FoRecoML or SLmetrics.
forestploter
A forest plot package that keeps handing users control of one more graphical detail.
ggInterval
Interval-valued data plotting, spending 2026 making its function names and examples survive CRAN.
rgm
A microbiome network model that got itself un-archived by deleting the dependency that killed it.
contagionchannels
Three releases in ten days, every one of them a CRAN reviewer's correction rather than a code change.
tidyaudit
Pipeline provenance for tidyverse workflows, recording what changed at each step without keeping the data.
See all FoRecoML alternatives → · See all SLmetrics alternatives →
Recent activity from FoRecoML and SLmetrics
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoFoRecoMLStructured print and summary for fitted reconciliation models
- 1mo agoFoRecoMLAdopts FoReco's foreco class for all reconciliation output
- 3mo agoFoRecoMLMachine-learning forecast reconciliation arrives on CRAN ⚡
- 1y agoSLmetricsArmadillo backend brings 5-20x speedups and an extensible metrics API ⚡
- 1y agoSLmetricsConsistent S3 signatures and three bundled datasets
- 1y agoSLmetricsRegression metrics 2-10x faster with reworked OpenMP controls
- 1y agoSLmetricsOpenMP parallelism and a soft-label entropy family
- 1y agoSLmetricsCross-entropy loss and relative RMSE with three normalisations
- 1y agoSLmetricsSample weights flow through the confusion matrix
Frequently asked questions
What is the difference between FoRecoML and SLmetrics?
Both compete on the same themes — machine-learning, r-package — within Infra & APIs. FoRecoML and SLmetrics are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is FoRecoML better than SLmetrics?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. FoRecoML and SLmetrics are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.
What are the best alternatives to FoRecoML?
Top FoRecoML alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "FoRecoML alternatives" section above for the current picks, or visit /alternatives/forecoml for the full list with editorial commentary on each.
What are the best alternatives to SLmetrics?
Top SLmetrics alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "SLmetrics alternatives" section above for the current picks, or visit /alternatives/slmetrics for the full list with editorial commentary on each.